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在深度神经网络的volatolomics中多目标预测:在修改的大气下,模拟由Brochothrix thermosphacta产生的挥发性有机化合物

Linyun Chen¹, Lotta Kuuliala², Christophe Walgraeve³

¹Research Unit Food Microbiology and Food Preservation (FMFP), Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium.

Food research international (Ottawa, Ont.)

|November 21, 2025

概括

这项研究引入了多目标预测 (MTP) 来预测来自肉类腐烂细菌的多种挥发性有机化合物 (VOC). 在各种包装条件下,MTP模型准确地预测VOC,从而提升了食品安全预测.

科学领域:

食品微生物学食品微生物学
计算化学的计算化学
数据科学数据科学数据科学

背景情况:

肉类的微生物腐烂会产生挥发性有机化合物 (VOC),导致不良气味和缩短保质期.
由于微生物新陈代谢对包装大气 (O2 / CO2) 的敏感性,预测volatolome具有挑战性.
传统的监督学习通常侧重于单个目标预测,限制了全面的volatolome分析.

研究的目的:

引入和评估多目标预测 (MTP) 用于预测由Brochothrix thermosphacta产生的多个VOC.
开发一种新的双分支神经网络,用于预测微生物数量,气体比率和VOC新陈代谢之间的相互作用.
评估MTP模型在预测各种包装大气中的VOC水平方面的有效性.

主要方法:

使用了840个猪肉模拟介质样本的数据集,这些样本被注射了B. thermosphacta.
在20种不同的O2/CO2/N2大气层下存储样品长达10天,记录总板数 (TPC),气体比率和VOC度.
为MTP开发了一个双分支的神经网络,集成微生物计数,气体比率和VOC代谢数据.

主要成果:

基于MTP的回归和分类模型成功预测了特定大气中的VOC水平.
模型使用来自19个不同的大气层的数据进行了训练和验证,证明了强大的预测能力.
该研究强调了MTP在食品系统中复杂的飞组预测方面的潜力.

结论:

多目标预测 (MTP) 显示出在食品工业中推进飞学方面显著的前景.
开发的神经网络有效地模拟了对预测微生物VOC产生至关重要的相互作用.
建议进一步研究真实肉类矩阵和全面的食品经济学数据,以提高预测准确度.

关键词:

微生物的腐败是微生物的腐败.多个目标预测预测.包装中的大气环境配对学习是一种双向学习.一个双分支的神经网络.飞行器是什么?飞行器是什么?

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